“With Python, you can create self-explanatory, concise, and engaging data visuals, and present insights that impact your business” - Tim Großmann and Mario Döbler [Interview]

Data today is the world’s most important resource. However, without properly visualizing your data to discover meaningful insights, it’s useless. Creating visualizations helps in getting a clearer and concise view of the data, making it more tangible for (non-technical) audiences. To further illustrate this, below are questions aimed at giving you an idea why data visualization is so important and why Python should be your choice.

In a recent interview, Tim Großmann and Mario Döbler, the authors of the course titled, ‘Data Visualization with Python’, shared with us the importance of Data visualization and why Python is the best fit to carry out Data Visualization.

Key Takeaways

• Data visualization is a great way, and sometimes the only way, to make sense of the constantly growing mountain of data being generated today.
• With Python, you can create self-explanatory, concise, and engaging data visuals, and present insights that impact your business.
• Your data visualizations will make information more tangible for the stakeholders while telling them an interesting story.
• Visualizations are a great tool to transfer your understanding of the data to a less technical co-worker. This builds a faster and better understanding of data.
• Python is the most requested and used language in the industry. Its ease of use and the speed at which you can manipulate and visualize data, combined with the number of available libraries makes Python the best choice.

Full Interview

Why is Data Visualization important? What problem is it solving?

As the amount of data grows, the need for developers with knowledge of data analytics and especially data visualization spikes. In recent years we have experienced an exponential growth of data. Currently, the amount of data doubles every two years. For example, more than eight thousand tweets are sent per second; and more than eight hundred photos are uploaded to Instagram per second. To cope with the large amounts of data, visualization is essential to make it more accessible and understandable.

Everyone has heard of the saying that a picture is worth a thousand words. Humans process visual data better and faster than any other type of data. Another important point is that data is not necessarily the same as information. Often people aren’t interested in the data, but in some information hidden in the data. Data visualization is a great tool to discover the hidden patterns and reveal the relevant information. It bridges the gap between quantitative data and human reasoning, or in other words, visualization turns data into meaningful information.

What other similar solutions or tools are out there? Why is Python better?

Data visualizations can be created in many ways using many different tools. MATLAB and R are two of the available languages that are heavily used in the field of data science and data visualization. There are also some non-coding tools like Tableau which are used to quickly create some basic visualizations. However, Python is the most requested and used language in the industry. Its ease of use and the speed at which you can manipulate and visualize data, combined with the number of available libraries makes Python the best choice. In addition to all the mentioned perks, Python has an incredibly big ecosystem with thousands of active developers.

Python really differs in a way that allows users to also build their own small additions to the tools they use, if necessary. There are examples of pretty much everything online for you to use, modify, and learn from.

How can Data Visualization help developers? Give specific examples of how it can solve a problem.

Working with, and especially understanding, large amounts of data can be a hard task. Without visualizations, this might even be impossible for some datasets. Especially if you need to transfer your understanding of the data to a less technical co-worker, visualizations are a great tool for a faster and better understanding of data.

In general, looking at your data visualized often speaks more than a thousand words.

Imagine getting a dataset which only consists of numerical columns. Getting some good insights into this data without visualizations is impossible. However, even with some simple plots, you can often improve your understanding of even the most difficult datasets.

Think back to the last time you had to give a presentation about your findings and all you had was a table with numerical values in it. You understood it, but your colleagues sat there and scratched their heads. Instead had you created some simple visualizations, you would have impressed the entire team with your results.

What are some best practices for learning/using Data Visualization with Python?

Some of the best practices you should keep in mind while visualizing data with Python are:

• Start looking and experimenting with examples
• Start from scratch and build on it
• Make full use of documentation
• Use every opportunity you have with data to visualize it

To know more about the best practices in detail, read our detailed notes on 4 tips for learning Data Visualization with Python.

What are some myths/misconceptions surrounding Data Visualization with Python?

• Data visualizations are just for data scientists
• Its technologies are difficult to learn
• Data visualization isn’t needed for data insights
• Data visualization takes a lot of time

Read about these myths in detail in our article, ‘Python Data Visualization myths you should know about’.

Data visualization in combination with Python is an essential skill when working with data. When properly utilized, it is a powerful combination that not only enables you to get better insights into your data but also gives you the tool to communicate results better. Data nowadays is everywhere so developers of every discipline should be able to work with it and understand it.

About the authors

Tim Großmann

Tim Großmann is a CS student with interest in diverse topics ranging from AI to IoT. He previously worked at the Bosch Center for Artificial Intelligence in Silicon Valley in the field of big data engineering. He’s highly involved in different Open Source projects and actively speaks at meetups and conferences about his projects and experiences.



Mario Döbler

Mario Döbler is a graduate student with a focus in deep learning and AI. He previously worked at the Bosch Center for Artificial Intelligence in Silicon Valley in the field of deep learning, using state-of-the-art algorithms to develop cutting-edge products. Currently, he dedicates himself to apply deep learning to medical data to make health care accessible to everyone.

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